CN106254723A - A kind of method of real-time monitoring video noise interference - Google Patents

Abstract

The method that the invention discloses the interference of a kind of real-time monitoring video noise, comprises the following steps: (1) input picture；(2) image of input is carried out piecemeal process；(3) minima N of eight directional operator convolution absolute values of the image after piecemeal is calculated；(4) judge whether this image is doubtful noise spot according to N value；(5) method judged by multiple dimensioned noise determines whether whether pixel exists noise spot；(6) output noise point terminating.The method that the invention provides the interference of a kind of real-time monitoring video noise, use the method that convolution operator and multiple dimensioned operator combine to carry out picture noise detection, make noise measuring more targeted, can preferably detect impulsive noise, Gaussian noise and white noise, substantially increase the effect of noise measuring in video and efficiency.

Description

A kind of method of real-time monitoring video noise interference

Technical field

The invention belongs to video noise detection field, the method specifically referring to the interference of a kind of real-time monitoring video noise.

Background technology

In a series of process of picture signal, either picture signal collection, change, store, transmit or other Processing mode, be all not fee from and disturbed by each noise like.On the one hand, noise causes human visual system to regard image Feel that the impression of quality is deteriorated；On the other hand, noise also can reduce successive image Processing Algorithm (image segmentation, compression, feature carries Take, detect identification etc.) effect；Serious sound pollution even can make some image processing algorithm complete failure or lose Meaning originally.

The impact visually of trace noise in image is the most inconspicuous, but these trace noises but be enough to be substantially reduced The efficiency of compression of images.It addition, for the transmission communication process of digital picture, the baneful influence that noise brings is the tightest Weight, the noise especially introduced in communication system front end, its power progressively can be amplified by follow-up system so that subsequent module receives To image in noise density very big, to such an extent as to human eye is all difficult to the content of identification image itself.Therefore, video is supervised For Ore-controlling Role, the noise occurred in video image detected in time, be important in inhibiting.

From the point of view of the angle of signal processing, noise can be divided into white noise, low-frequency noise, high-frequency noise and single-frequency noise Deng.Gaussian noise, impulsive noise (salt-pepper noise), rayleigh noise, gamma can be divided into again to make an uproar according to probability density estimation Sound, index noise, uniformly distributed noise etc..Find that video monitoring image occurs impulsive noise, height according to Practical Project test The probability of this noise and white noise is relatively big, occurs that the probability of other noise is relatively small a lot.So, nowadays need a kind of method Complete impulsive noise, Gaussian noise and white noise to occurring in video monitoring image to detect.

Emphasis of the present invention relates generally to a kind of method using convolution operator and multiple dimensioned operator to combine to carry out image Noise measuring.

Summary of the invention

It is an object of the invention to overcome the problems referred to above, it is provided that the method for a kind of real-time monitoring video noise interference, adopt The method combined with convolution operator and multiple dimensioned operator carries out picture noise detection so that noise measuring is more targeted, Can preferably detect impulsive noise, Gaussian noise and white noise, substantially increase to the effect of noise measuring in video with Efficiency.

The purpose of the present invention is achieved through the following technical solutions:

The method of a kind of real-time monitoring video noise interference, comprises the following steps:

(1) input picture；

(2) image of input is carried out piecemeal process；

(3) minima N of eight directional operator convolution absolute values of the image after piecemeal is calculated；

(4) judge whether this image is doubtful noise spot according to N value, if not doubtful noise spot is then normal picture pixel Point, if doubtful noise spot then enters step (5)；

(5) method judged by multiple dimensioned noise determines whether whether pixel exists noise spot, if not existing, For normal pixel point, if there is noise spot, enter step (6)；

(6) output noise point terminating.

Step (3) is by using eight directional operators detect roughly in image whether there is noise spot；Wherein, in inciting somebody to action Heart black pixel point value is 8, and gray pixels point value is-1, and white pixel point value is 0；Eight directional operators correspond to not With edge direction, describe for convenience, these eight directional operators be denoted as K_i, i=1,2 ..., 8；

If the size of digital picture X to be detected is M × N, wherein (x, y) pixel value at place is f to pixel_xy, with point (x, y) Centered by 9x9 detection window in all grey scale pixel values constitute set W be:

W={f_{X+s, y+t}Shu-2≤s≤2 ,-2≤t≤2, (x, y) ∈ X},

W and above-mentioned eight directional operators do convolution, take the minima of eight convolution absolute values, defeated as this pixel Go out to be worth N (x, y), it may be assumed that

N (x, y)=min (N₁(x, y), N₂(x, y) ..., N₈(x, y)), (1)

Wherein, N_i(x, y)=f_xy*K_i, i=1,2 ..., 8, indicate direction core K_iWith (x, y) centered by 9 × 9 artwork Convolution as pixel.

First an experience threshold values T is given, by output valve N of the pixel in step (3) with given in step (4) Experience threshold values T compares, when output valve N of pixel is more than the empirical value T given, and can be by this pixel (x, y) division For doubtful noise spot, and enter follow-up step (5)；Otherwise, assert that (x y) is normal image slices vegetarian refreshments to this pixel.

In step (5) first in above-mentioned all directions in 9 × 9 regional areas of detective operators, take the subgraph of 8 × 8 and start, press According to 8 × 8, the yardstick of 4 × 4,2 × 2,1 × 1 decomposes successively, until the impulsive noise quantity in subgraph is less than or equal to 2, when Whether front subgraph exists impulsive noise, be by by the pixel value of pixels all in current subgraph with the mean pixel of subgraph Value compares and judges；

Judge pixel in subgraph (x, y) be whether impulsive noise according to being:

Wherein,

Sized by be the average pixel value of subgraph of P × Q, its formula is:

$\stackrel{\‾}{m}=\frac{1}{P\×Q}{\mathrm{\Σ}}_{i=1}^P{\mathrm{\Σ}}_{j=1}^{Q}f(i,j),$

F (i, j) pixel in representative image (i, j) pixel value at place,

σ is the standard variance of subgraph, and its computing formula is:

$\mathrm{\σ}=\sqrt{\frac{1}{P\×Q}{\mathrm{\Σ}}_{i=1}^P{\mathrm{\Σ}}_{j=1}^Q{(\stackrel{\‾}{m}-f(i,j\left)\right)}^2}.$

The present invention compared with prior art, has the following advantages and beneficial effect:

The method that the method employing convolution operator of the present invention and multiple dimensioned operator combine is to carry out picture noise detection, greatly Improve greatly the specific aim of noise measuring so that impulsive noise therein, Gaussian noise and white noise can by more quick with It is detected accurately, substantially increases the effect of noise measuring in video image and efficiency, reduce video image Testing cost.

Accompanying drawing explanation

Fig. 1 is the flow chart of the present invention.

Fig. 2 is the schematic diagram of eight directional operators of the present invention.

Fig. 3 is the schematic diagram of the multiple dimensioned noise judgement of the present invention.

Detailed description of the invention

Below in conjunction with embodiment, the present invention is described in further detail, but embodiments of the present invention are not limited to this.

Embodiment

As it is shown in figure 1, the method for a kind of real-time monitoring video noise interference, comprise the following steps:

(1) input picture.

(2) image of input is carried out piecemeal process；

Common knowledge in size according to image and industry, is laterally divided into 10 deciles, or 15 deciles by image, or 20 etc. Grade；Longitudinally do same treatment, thus form piecemeal.

(3) minima N of eight directional operator convolution absolute values of the image after piecemeal is calculated；

Step (3) is by using eight directional operators detect roughly in image whether there is noise spot；

As in figure 2 it is shown, wherein, being 8 by central black pixel value, gray pixels point value is-1, white pixel point Value is 0；Eight directional operators correspond to different edge directions, describes for convenience, by the sign of these eight directional operators For K_i, i=1,2 ..., 8；

If the size of digital picture X to be detected is M × N, wherein (x, y) pixel value at place is f to pixel_xy, with point (x, y) Centered by 9x9 detection window in all grey scale pixel values constitute set W be:

W={f_{X+s, y+t}Shu-2≤s≤2 ,-2≤t≤2, (x, y) ∈ X},

The span of s and t is-2 to+2, then, x+s, y+t be exactly with pixel (x, y) centered by 5x5 partial zones Territory；

W and above-mentioned eight directional operators do convolution, take the minima of eight convolution absolute values, defeated as this pixel Go out to be worth N (x, y), it may be assumed that

N (x, y)=min (N₁(x, y), N₂(x, y) ..., N₈(x, y)), (1)

Wherein, N_i(x, y)=f_xy*K_i, i=1,2 ..., 8, indicate direction core K_iWith (x, y) centered by 9 × 9 artwork Convolution as pixel.

Formula (1) why can be used for detecting pixel (whether x, be y) noise spot, and main cause is as follows: if (x, y) It is the image slices vegetarian refreshments in local smoothing method region, then the value of these eight convolution all should be the least, and is essentially close to 0, therefore N (x, value y) also can be the least；If (x, y) is the pixel on certain edge, then it and the volume in this edge edge direction Long-pending meeting is the least, and therefore, (x, value y) is the least for N；If but (x, y) is a noise spot, then it is with eight direction cores Convolution value all can be very big, and therefore (x, value y) also can be the biggest for N.

(4) judge whether this image is doubtful noise spot according to N value, if not doubtful noise spot is then normal picture pixel Point, if doubtful noise spot then enters step (5)；

First an experience threshold values T is given, by output valve N of the pixel in step (3) with given in step (4) Experience threshold values T compares, when output valve N of pixel is more than the empirical value T given, and can be by this pixel (x, y) division For doubtful noise spot, and enter follow-up step (5)；Otherwise, assert that (x y) is normal image slices vegetarian refreshments to this pixel.This The empirical value T at place, obtains through substantial amounts of actual test, belongs to the common knowledge in industry.

In view of for a width is actual by the image of sound pollution, although utilize said method can substantially judge be No have whether some pixel exists noise jamming, but this judgement is relatively coarse, therefore, it is necessary to utilize multiple dimensioned noise to sentence Disconnected method judges further, in order to be accurately obtained whether certain region exists the judgement of noise.

(5) method judged by multiple dimensioned noise determines whether whether pixel exists noise spot, if not existing, For normal pixel point, if there is noise spot, enter step (6)；

First, in above-mentioned all directions in 9 × 9 regional areas of detective operators, take the subgraph of 8 × 8 and start, according to 8 × 8,4 The yardstick of × 4,2 × 2,1 × 1 decomposes successively, until the impulsive noise quantity in subgraph is less than or equal to 2, in current subgraph Whether there is impulsive noise, be by by the pixel value of pixels all in current subgraph with the average pixel value of subgraph compares Judge；

Being exactly the adaptive decomposition process schematic of 8 × 8 subgraphs in Fig. 3, in figure, leftmost digitized representation is current Detection yardstick, inside node digitized representation subgraph numbering, the N on the right of node represents the impulsive noise number in current subgraph, Dotted line node on behalf needs the subgraph continuing to decompose, and solid line node on behalf need not the subgraph decomposed again.

Judge pixel in subgraph (x, y) be whether impulsive noise according to being:

Wherein,

Sized by be the average pixel value of subgraph of P × Q, its formula is:

$\stackrel{\‾}{m}=\frac{1}{P\×Q}{\mathrm{\Σ}}_{i=1}^P{\mathrm{\Σ}}_{j=1}^{Q}f(i,j),$

F (i, j) pixel in representative image (i, j) pixel value at place, σ is the standard variance of subgraph, and its computing formula is:

$\mathrm{\σ}=\sqrt{\frac{1}{P\×Q}{\mathrm{\Σ}}_{i=1}^P{\mathrm{\Σ}}_{j=1}^Q{(\stackrel{\‾}{m}-f(i,j\left)\right)}^2}.$

(6) output noise point terminating.

As it has been described above, just can well realize the present invention.

Claims (4)

1. the method for a real-time monitoring video noise interference, it is characterised in that: comprise the following steps:

(1) input picture；

(2) image of input is carried out piecemeal process；

(3) minima N of eight directional operator convolution absolute values of the image after piecemeal is calculated；

(4) judge whether this image is doubtful noise spot according to N value, if not doubtful noise spot is then normal picture pixel, If doubtful noise spot then enters step (5)；

(5) method judged by multiple dimensioned noise determines whether whether pixel exists noise spot, if not existing, is just Often pixel, if there is noise spot, enters step (6)；

(6) output noise point terminating.

The method of a kind of real-time monitoring video noise the most according to claim 1 interference, it is characterised in that: step (3) is By using eight directional operators detect roughly in image whether there is noise spot；Wherein, by central black pixel value Being 8, gray pixels point value is-1, and white pixel point value is 0；Eight directional operators correspond to different edge directions, for Facilitate description, these eight directional operators are denoted as K_i, i=1,2 ..., 8；

If the size of digital picture X to be detected is M × N, wherein (x, y) pixel value at place is f to pixel_xy, with point, (x, in y) being In the 9x9 detection window of the heart, all grey scale pixel values composition set W is:

W={f_{X+s, y+t}Shu-2≤s≤2 ,-2≤t≤2, (x, y) ∈ X},

W and above-mentioned eight directional operators do convolution, take the minima of eight convolution absolute values, as the output valve of this pixel N (x, y), it may be assumed that

N (x, y)=min (N₁(x, y), N₂(x, y) ..., N₈(x, y)), (1)

Wherein, N_i(x, y)=f_xy*K_i, i=1,2 ..., 8, indicate direction core K_iWith (x, y) centered by 9 × 9 original image pictures The convolution of element.

The method of a kind of real-time monitoring video noise the most according to claim 2 interference, it is characterised in that: in step (4) In first provide an experience threshold values T, output valve N of the pixel in step (3) is compared with given experience threshold values T, When output valve N of pixel is more than given empirical value T, can (x y) be divided into doubtful noise spot, goes forward side by side by this pixel Enter follow-up step (5)；Otherwise, assert that (x y) is normal image slices vegetarian refreshments to this pixel.

The method of a kind of real-time monitoring video noise the most according to claim 3 interference, it is characterised in that: in step (5) First, in above-mentioned all directions in 9 × 9 regional areas of detective operators, take the subgraph of 8 × 8 and start, according to 8 × 8,4 × 4,2 × 2, The yardstick of 1 × 1 decomposes successively, until the impulsive noise quantity in subgraph is less than or equal to 2, whether there is arteries and veins in current subgraph Rush noise, be by the pixel value of pixels all in current subgraph is judged with the average pixel value of subgraph compares；

Judge pixel in subgraph (x, y) be whether impulsive noise according to being:

Wherein,

Sized by be the average pixel value of subgraph of P × Q, its formula is:

$\stackrel{\‾}{m}=\frac{1}{P\×Q}{\mathrm{\Σ}}_{i=1}^P{\mathrm{\Σ}}_{j=1}^{Q}f(i,j),$

F (i, j) pixel in representative image (i, j) pixel value at place,

σ is the standard variance of subgraph, and its computing formula is:

$\mathrm{\σ}=\sqrt{\frac{1}{P\×Q}{\mathrm{\Σ}}_{i=1}^P{\mathrm{\Σ}}_{j=1}^Q{(\stackrel{\‾}{m}-f(i,j\left)\right)}^2}.$